GAMMA: gap-aware motif mining under incomplete labeling with applications to MHC motifs.

ABSTRACT:

Motivation

Sequence motif identification is crucial for understanding molecular recognition, particularly in immune responses involving peptide binding to major histocompatibility complex (MHC) Class I molecules for antigen presentation to T cells. Traditionally, MHC Class I binding motifs are assumed to be contiguous and span nine amino acids. However, structural evidence suggests that binding may involve nonadjacent residues, challenging the assumptions of existing methods.

Results

In this study, we propose Gap-Aware Motif Mining Algorithm (GAMMA), a probabilistic framework designed to identify noncontiguous motifs under conditions of incomplete labeling. GAMMA employs Bayesian inference with Markov chain Monte Carlo sampling to jointly estimate motif parameters, binding l

SUBMITTER: Tang X 

PROVIDER: S-EPMC12866627 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature